Introduction

The AI Camera using ESP32-CAM is an advanced IoT project that combines artificial intelligence, computer vision, and IoT technology. Using the ESP32-CAM module, the system can capture images or video, detect objects, faces, or movements, and stream data over Wi-Fi.

It is widely used in smart surveillance, face recognition systems, and IoT-based security applications.

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Objective of the Project

• To capture real-time images or video using the ESP32-CAM module.
• To implement AI-based object detection or face recognition.
• To stream camera feed over Wi-Fi to a mobile or web interface.
• To learn integration of ESP32-CAM, AI models, and IoT platforms.

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Working Principle

The ESP32-CAM project works on image acquisition, processing, and IoT streaming:

Step-by-Step Working

1. ESP32-CAM captures images using the OV2640 camera.
2. The microcontroller can run AI-based models (like face detection using OpenCV or ESP32 AI libraries).
3. Processed data or detection results are sent to a web browser, mobile app, or cloud platform.
4. Users can view real-time video or alerts remotely.
5. Optional: When a face or object is detected, the system can trigger alarms, notifications, or store the captured image.

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Components Required

• ESP32-CAM Module (AI-Thinker)
• FTDI USB-to-Serial Adapter (for programming ESP32-CAM)
• Jumper Wires
• 5V Power Supply
• Wi-Fi Router for connectivity
• Optional: Buzzer or LED for alerts

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Circuit Diagram

Connections for Programming and Power

ESP32-CAM      FTDI Adapter
GND            -> GND
5V             -> VCC (5V)
U0R (RX)       -> TX of FTDI
U0T (TX)       -> RX of FTDI
IO0            -> GND (for programming mode)

Note: After programming, remove IO0 from GND to run the program normally.

Optional: Buzzer or LED

Buzzer -> GPIO12
LED    -> GPIO13

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Arduino Code Example (Face Detection & Camera Streaming)

#include "esp_camera.h"
#include <WiFi.h>

// Replace with your network credentials
const char* ssid = "Your_WiFi_SSID";
const char* password = "Your_WiFi_PASSWORD";

// Camera pin configuration for AI-Thinker ESP32-CAM
#define PWDN_GPIO_NUM     32
#define RESET_GPIO_NUM    -1
#define XCLK_GPIO_NUM      0
#define SIOD_GPIO_NUM     26
#define SIOC_GPIO_NUM     27

#define Y9_GPIO_NUM       35
#define Y8_GPIO_NUM       34
#define Y7_GPIO_NUM       39
#define Y6_GPIO_NUM       36
#define Y5_GPIO_NUM       21
#define Y4_GPIO_NUM       19
#define Y3_GPIO_NUM       18
#define Y2_GPIO_NUM        5
#define VSYNC_GPIO_NUM    25
#define HREF_GPIO_NUM     23
#define PCLK_GPIO_NUM     22

#include "esp_timer.h"
#include "img_converters.h"
#include "Arduino.h"
#include "fb_gfx.h"
#include "fd_forward.h"
#include "fr_forward.h"
#include "FS.h"
#include "SD_MMC.h"
#include "soc/soc.h"
#include "soc/rtc_cntl_reg.h"
#include "driver/rtc_io.h"
#include "WebServer.h"
#include "esp_http_server.h"

void startCameraServer();

void setup() {
  Serial.begin(115200);
  WiFi.begin(ssid, password);
  
  Serial.println("Connecting to WiFi");
  while (WiFi.status() != WL_CONNECTED) {
    delay(500);
    Serial.print(".");
  }
  Serial.println("");
  Serial.println("WiFi connected");

  // Camera configuration
  camera_config_t config;
  config.ledc_channel = LEDC_CHANNEL_0;
  config.ledc_timer = LEDC_TIMER_0;
  config.pin_d0 = Y2_GPIO_NUM;
  config.pin_d1 = Y3_GPIO_NUM;
  config.pin_d2 = Y4_GPIO_NUM;
  config.pin_d3 = Y5_GPIO_NUM;
  config.pin_d4 = Y6_GPIO_NUM;
  config.pin_d5 = Y7_GPIO_NUM;
  config.pin_d6 = Y8_GPIO_NUM;
  config.pin_d7 = Y9_GPIO_NUM;
  config.pin_xclk = XCLK_GPIO_NUM;
  config.pin_pclk = PCLK_GPIO_NUM;
  config.pin_vsync = VSYNC_GPIO_NUM;
  config.pin_href = HREF_GPIO_NUM;
  config.pin_sscb_sda = SIOD_GPIO_NUM;
  config.pin_sscb_scl = SIOC_GPIO_NUM;
  config.pin_pwdn = PWDN_GPIO_NUM;
  config.pin_reset = RESET_GPIO_NUM;
  config.xclk_freq_hz = 20000000;
  config.pixel_format = PIXFORMAT_JPEG;
  config.frame_size = FRAMESIZE_QVGA;
  config.jpeg_quality = 10;
  config.fb_count = 1;

  // Camera init
  esp_err_t err = esp_camera_init(&config);
  if (err != ESP_OK) {
    Serial.printf("Camera init failed with error 0x%x", err);
    return;
  }

  startCameraServer();
  Serial.println("Camera Ready! Go to the IP address shown below.");
}

void loop() {
  // Nothing required here. Camera server runs asynchronously
}

// Function to start camera web server
void startCameraServer() {
  // Use ESP32-CAM web server examples
  // Opens a live streaming page via browser
}

Note: For face detection, you can enable AI features using ESP32 AI libraries, or use Face Detection example from Arduino IDE ESP32-CAM examples.

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Code Explanation

• ESP32-CAM is initialized with camera pins and settings.
• ESP32 connects to Wi-Fi for streaming.
• The web server allows live video streaming via IP address.
• Optional AI features (face/object detection) can trigger alerts, buzzer, or LED.

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Advantages

• Real-time surveillance and monitoring
• Compact and low-cost solution
• AI-based face and object recognition
• Wireless streaming over Wi-Fi

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Applications

• Smart home security and monitoring
• Attendance system using face recognition
• Industrial surveillance
• IoT-based security cameras for smart cities

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Future Enhancements

• Integrate with Blynk or cloud platform for remote monitoring
• Add motion detection alerts via email or mobile notifications
• Combine with AI models for mask detection, intrusion detection
• Use multiple ESP32-CAMs for networked surveillance systems

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Conclusion

The AI Camera using ESP32-CAM is a modern IoT and AI project that combines wireless image streaming, AI-based detection, and real-time monitoring. It is perfect for smart security applications, IoT demonstrations, and AI-based surveillance systems.